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Technical Trading

Does technical trading work, or not? Rationalists dismiss it; behavioralists investigate it. Is there any verdict? These blog entries relate to technical trading.

Are Bad Weeks (Months) Followed by Bad or Good Ones?

Is a bad week or month in the stock market an indicator of further immediate deterioration? Using weekly and monthly S&P 500 index closing levels since 1950 (2,998 weeks and 689 months), we find that: Keep Reading

The 52-Week High as a Momentum Indicator for Individual Stocks

A reader notes and asks: “It is frequently said that stocks at 52-week highs are the most likely to outperform in the future. Is there any academic evidence to support this assertion?” In their October 2004 Journal of Finance article entitled “The 52-Week High and Momentum Investing”, Thomas George and Chuan-Yang Hwang examine the explanatory power of the 52-week high in the context of momentum investing. They compare the 52-week high as a momentum indicator to benchmark momentum strategies that employ six months of past returns to forecast six months of future returns. Using price data for a broad range of stocks over the period 1963-2001, they find that: Keep Reading

Technical Analysis: “Anathema to the Academic World”?

Technical analysis seeks to exploit stock mispricings derived from postulated investor/trader psychological biases. Does short-term technical analysis actually produce abnormal returns? Or, do its adherents persist based on a misperception that they are to some degree in control of random rewards. In their February 2006 paper entitled “Does Intraday Technical Analysis in the U.S. Equity Market Have Value?”, Ben Marshall, Rochester Cahan and Jared Cahan investigate whether intraday technical analysis is profitable in the overall U.S. equity market. Specifically, they apply a combination of statistically rigorous bootstrapping tests to 7,846 trading rules from five rule families (Filter, Moving Average, Support and Resistance, Channel Breakouts, and On-Balance Volume). Using 5-minute data for Standard and Poor’s Depository Receipts (SPDR) over the period 1/1/02-12/31/03 (encompassing both bear and bull trends), they conclude that: Keep Reading

Testing the Head-and-Shoulders Pattern

Does the head-and-shoulders stock price pattern embody investor attitudes that traders can exploit to earn abnormal returns? Or, does it represent an opportunity for the statistics-challenged to be fooled by randomness? In their October 2006 paper entitled “The Predictive Power of ‘Head-and-Shoulders’ Price Patterns in the U.S. Stock Market”, Gene Savin, Paul Weller and Janis Zvingelis use a pattern recognition algorithm, as filtered based on the experience of a technical analyst, to determine whether head-and-shoulders price patterns formed across intervals of 63 trading days have predictive power for future stock returns over the next few months. Using daily price data during 1990-1999 for all stocks in the S&P 500 and Russell 2000 indexes as of June 1990, they conclude that: Keep Reading

Candlesticks? Fiddlesticks!

Does candlestick technical analysis (examining relationships among opening, high, low and closing prices over the past 1-3 days to identify continuation and reversal signals) generate abnormal returns? In their recent paper entitled “Market Timing with Candlestick Technical Analysis”, Ben Marshall, Martin Young and Lawrence Rose test the profitability of trading stocks included in the Dow Jones Industrial Average based on 28 different candlestick signals. They assume a ten-day holding period after trading at the close on the day after a signal appears. Using stock price data for 1/1/92-12/31/02, they conclude that: Keep Reading

Evidence-Based Technical Analysis: Applying the Scientific Method and Statistical Inference to Trading Signals (Chapter-by-Chapter Review)

In his 2007 book Evidence-Based Technical Analysis: Applying the Scientific Method and Statistical Inference to Trading Signals, David Aronson opens with two contentions: (1) “much of the wisdom comprising the popular version of TA does not qualify as legitimate knowledge;” and, (2) “TA must evolve into a rigorous observational science if it is to deliver on its claims and remain relevant.” Taken in parts, this book offers sound methods for analysis. Taken as an integrating whole, it offers insightful context for evaluating a broad range of financial analyses/claims presented by others. Here is a chapter-by-chapter review of some of the insights in this book: Keep Reading

Classic Papers: Returns from Pattern-Based Technical Analysis?

Are trades based on complex technical patterns, such as head-and-shoulders, rational speculations or noise? In other words, do such patterns reliably indicate opportunities to capture excess returns? In her July 1998 paper entitled “Identifying Noise Traders: The Head-And-Shoulders Pattern in U.S. Equities”, Carol Osler investigates whether head-and-shoulders trading is significant and whether it is profitable. In their August 2000 paper entitled “Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation”, Andrew Lo, Harry Mamaysky and Jiang Wang apply advanced empirical methods (compare with fingerprint identification or face recognition) to evaluate technical analysis patterns such as head-and-shoulders and double-bottoms. These papers conclude that: Keep Reading

Technical Analysis as Folk Medicine

Is there a way to end the endless debate on the merits of technical analysis? In his September 2006 paper entitled “On the Analogy Between Scientific Study of Technical Analysis and Ethnopharmacology”, Waldemar Stronka proposes bringing technical analysis into the financial economics fold in a manner analogous to the successful incorporation of folk medicine by pharmacology. Specifically, he notes that: Keep Reading

Does Technical Trading Work for Certain Kinds of Stocks?

Can technical traders make money if they focus on stocks that are small, illiquid or in specific industries? In their September 2006 paper entitled “Is Technical Analysis Profitable on U.S. Stocks with Certain Size, Liquidity or Industry Characteristics?”, Ben Marshall, Sun Qian and Martin Young test three widely used technical trading rules: (1) the variable length moving average rule: (2) the fixed length moving average rule; and, (3) the trading range break-out rule. Using daily close data for 1,065 NYSE and NASDAQ stocks trading over the entire period 1990-2004, they find that: Keep Reading

Testing the Indicators of Barchart.com

Barchart.com offers free short-term, intermediate-term and long-term technical assessments of stocks and exchange traded funds (ETF). Barchart.com, Inc. claims that their “market information is being used by millions of investors every month.” An obstacle to assessing the usefulness of their technical indicators is unavailability of historical data. To overcome this obstacle, we have recorded their average indicators for S&P 500 Depository Receipts (SPY) daily to assemble a statistically meaningful history for that ETF, which tracks the S&P 500 index. Whenever an indicator average is “Hold,” we assign a value of 0%. From the seven months of data collected, encompassing both market advances and declines, we conclude that: Keep Reading

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